Nikolay Tyukaevanddomi2000
ef45b5da8d
* Doc Migration from Gitlab (#1289) * doc migration * fix * Update FakeQuantize_1.md * Update performance_benchmarks.md * Updates graphs for FPGA * Update performance_benchmarks.md * Change DL Workbench structure (#1) * Changed DL Workbench structure * Fixed tags * fixes * Update ie_docs.xml * Update performance_benchmarks_faq.md * Fixes in DL Workbench layout * Fixes for CVS-31290 * [DL Workbench] Minor correction * Fix for CVS-30955 * Added nGraph deprecation notice as requested by Zoe * fix broken links in api doxy layouts * CVS-31131 fixes * Additional fixes * Fixed POT TOC * Update PAC_Configure.md PAC DCP 1.2.1 install guide. * Update inference_engine_intro.md * fix broken link * Update opset.md * fix * added opset4 to layout * added new opsets to layout, set labels for them * Update VisionAcceleratorFPGA_Configure.md Updated from 2020.3 to 2020.4 Co-authored-by: domi2000 <domi2000@users.noreply.github.com>
54 lines
2.1 KiB
Markdown
54 lines
2.1 KiB
Markdown
## EmbeddingBagPackedSum <a name="EmbeddingBagPackedSum"></a> {#openvino_docs_ops_sparse_EmbeddingBagPackedSum_3}
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**Versioned name**: *EmbeddingBagPackedSum-3*
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**Category**: *Sparse*
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**Short description**: Computes sums of "bags" of embeddings, without instantiating the intermediate embeddings.
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**Detailed description**: This is the first case of the PyTorch [EmbeddingBag](https://pytorch.org/docs/stable/nn.html#embeddingbag), it has indices in the tensor of format `[batch, indices_per_bag]`. If 3rd input is not provided, this operation is equivalent to *Gather* followed by *ReduceSum(axis=0)*. However, *EmbeddingBagPackedSum* is much more time and memory efficient than using a chain of these operations.
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**Inputs**:
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* **1**: `emb_table` tensor containing the embedding lookup table of the module of shape `[num_emb, emb_dim1, emb_dim2, ...]` and of type *T*. Required.
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* **2**: `indices` tensor of shape `[batch, indices_per_bag]` and of type *T_IND*. Required.
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* **3**: `per_sample_weights` tensor of the same shape as `indices` and of type *T*. Each value in this tensor are multiplied with each value pooled from embedding table for each index. Optional, default is tensor of ones.
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**Outputs**:
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* **1**: tensor of shape `[batch, emb_dim1, emb_dim2, ...]` and of type *T* containing embeddings for each bag.
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**Types**
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* *T*: any numeric type.
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* *T_IND*: `int32` or `int64`.
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**Example**
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```xml
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<layer ... type="EmbeddingBagPackedSum" ... >
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<input>
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<port id="0"> <!-- emb_table value is: [[-0.2, -0.6], [-0.1, -0.4], [-1.9, -1.8], [-1., 1.5], [ 0.8, -0.7]] -->
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<dim>5</dim>
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<dim>2</dim>
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</port>
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<port id="1"> <!-- indices value is: [[0, 2], [1, 2], [3, 4]] -->
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<dim>3</dim>
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<dim>2</dim>
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</port>
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<port id="2"/> <!-- per_sample_weigths value is: [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5]] -->
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<dim>3</dim>
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<dim>2</dim>
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</port>
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</input>
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<output>
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<port id="4"> <!-- output value is: [[-1.05, -1.2], [-1., -1.1], [-0.1, 0.4]] -->
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<dim>3</dim>
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<dim>2</dim>
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</port>
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</output>
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</layer>
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``` |